PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation

Jonathon Luiten, Paul Voigtlaender, Bastian Leibe
Asian Conference on Computer Vision (ACCV)

We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations. Towards this goal, we present the PReMVOS algorithm (Proposalgeneration, Refinement and Merging for Video Object Segmentation). Our method separates this problem into two steps, first generating a set of accurate object segmentation mask proposals for each video frame and then selecting and merging these proposals into accurate and temporally consistent pixel-wise object tracks over a video sequence in a way which is designed to specifically tackle the difficult challenges involved with segmenting multiple objects across a video sequence. Our approach surpasses all previous state-of-the-art results on the DAVIS 2017 video object egmentation benchmark with a J & F mean score of 71.6 on the test-dev dataset, and achieves first place in both the DAVIS 2018 Video Object Segmentation Challenge and the YouTube-VOS 1st Large-scale Video Object Segmentation Challenge.

» Show BibTeX

title={PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation},
author={Jonathon Luiten and Paul Voigtlaender and Bastian Leibe},
booktitle={Asian Conference on Computer Vision},

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